Snake robots enable mobility through extreme terrains and confined environments in terrestrial and space applications. However, robust perception and localization for snake robots remain an open challenge due to the proximity of the sensor payload to the ground coupled with a limited field of view. To address this issue, we propose Blind-motion with Intermittently Scheduled Scans (BLISS) which combines proprioception-only mobility with intermittent scans to be resilient against both localization failures and collision risks. BLISS is formulated as an integrated task and motion planning (TAMP) problem that leads to a chance-constrained hybrid partially observable Markov decision process (CC-HPOMDP), known to be computationally intractable due to the curse of history. Our novelty lies in reformulating CC-HPOMDP as a tractable, convex mixed integer linear program. This allows us to solve BLISS-TAMP significantly faster and jointly derive optimal task-motion plans. Simulations and hardware experiments on the EELS snake robot show our method achieves over an order of magnitude computational improvement compared to state-of-the-art POMDP planners and$>50 \%$better navigation time optimality versus classical two-stage planners.

The Exobiology Extant Life Surveyor (EELS) is a snake-like mobile instruments platform under development at Jet Propulsion Laboratory (JPL) for a mission concept to find evidence of life on Saturn's sixth largest moon, Enceladus. To conduct a life surveying mission there, the EELS platform must first traverse an unknown icy surface terrain before undertaking a controlled descent into a cryovolcanic vent. The remoteness of Enceladus and the icy nature of its terrain demands a level of autonomy in navigation significantly higher than previous rover missions. The perception system onboard EELS must be highly resilient to perceptually-degraded environments such as flat, open ice fields, icy plumes, and repeating geometries in vents. EELS' perception system is implemented as a multi-sensor Simultaneous Localisation And Mapping (SLAM) solution called SERPENT. State Estimation through Robust Perception in Extreme and Novel Terrains (SERPENT) estimates the robot trajectory and maintains a map database, from which dense global or local maps can be obtained on demand for downstream planning algorithms. This system opts to incorporate measurements from many sensor modalities (laser scans, images, IMU, altimeter, etc.), solving the SLAM problem through joint optimisation, and thus requires that the contribution of each sensor be balanced through careful modelling of their uncertainties. With a specific focus on Light Detection And Ranging (LiDAR) in this context, this paper proposes a principled approach to model the covariances of point-to-plane Iterative Closest Point (ICP). It performs a rigorous comparative analysis of new and existing covariance models, and is the first time some of these have been tested within a complete SLAM pipeline. These models are evaluated on perceptually challenging datasets collected in glacial environments by the EELS sensor suite (see Figures 1, 2). SERPENT is open-sourced at https://github.com/jpl-eels/serpent.

The discovery of ocean worlds such as Enceladus, Titan, and Europa motivates the development of versatile autonomous mobility systems to enable the next era of space exploration where there is large uncertainty in terrain specifications due to a lack of prior surface reconnaissance missions. To explore these environments, we propose Exobiology Extant Life Surveyor (EELS): the first large-scale (4 lm long with 400 Nm peak torque) snake robot. The large scale is achieved by using a screw-based active skin mechanism to decouple motion and shape control. Autonomous mobility for such a system remains an open problem due to its many Degrees of Freedom (DoFs), complex terrain interactions, and intermittent localization failures in GPS-denied perceptually degraded environments due to the presence of fog, dust, featureless terrains, etc. We propose NEO, an autonomy architecture that scales to large DoFs to generate a versatile set of gaits to achieve mobility in unknown extreme environments. We also discuss the resilience capabilities of NEO that achieves closed-loop tracking performance by leveraging exteroception when available but can also operate with proprioception only, leading to resiliency against localization failures via graceful degradation in performance rather than unsafe behaviors. A quantitative hardware evaluation of exteroceptive leader-follower gait is performed indoors on synthetic ice along with qualitative results of field deployment of the proprioceptive leader-follower and sidewinding gaits in extreme environments of icy and sandy terrains with mobility-stressing elements such as trenches, undulations, and steep slopes (up to 35 degrees). We present a set of lessons learned from field deployments with a summary of challenges and open research problems. Video: www.rohanthakker.in/eels-neo-autonomy.html

Deep learning has enjoyed much recent success, and applying state-of-the-art model learning methods to controls is an exciting prospect. However, there is a strong reluctance to use these methods on safety-critical systems, which have constraints on safety, stability, and real-time performance. We propose a framework which satisfies these constraints while allowing the use of deep neural networks for learning model uncertainties. Central to our method is the use of Bayesian model learning, which provides an avenue for maintaining appropriate degrees of caution in the face of the unknown. In the proposed approach, we develop an adaptive control framework leveraging the theory of stochastic CLFs (Control Lyapunov Functions) and stochastic CBFs (Control Barrier Functions) along with tractable Bayesian model learning via Gaussian Processes or Bayesian neural networks. Under reasonable assumptions, we guarantee stability and safety while adapting to unknown dynamics with probability 1. We demonstrate this architecture for high-speed terrestrial mobility targeting potential applications in safety-critical high-speed Mars rover missions.

Thermal cameras can enable autonomous flight at night without GPS. However, image-based navigation in the thermal infrared spectrum has been researched significantly less than in the visible spectrum. In this paper, we demonstrate closed-loop controlled outdoor flights at night on a quadrotor. Our state estimator can tightly couple inertial data with either thermal images at nighttime, or visual images at daytime. It is integrated in an autonomy framework for motion planning and control, which runs in real time on a standard embedded computer. We analyze thermal-inertial odometry performance extensively from sunset to sunrise, for various thermal non-uniformity levels, and compare it to visual-inertial odometry at daytime.

Hybrid ground and aerial vehicles can possess distinct advantages over ground-only or flight-only designs in terms of energy savings and increased mobility. In this work we outline our unified framework for controls, planning, and autonomy of hybrid ground/air vehicles. Our contribution is three-fold: 1) We develop a control scheme for the control of passive two-wheeled hybrid ground/aerial vehicles. 2) We present a unified planner for both rolling and flying by leveraging differential flatness mappings. 3) We conduct experiments leveraging mapping and global planning for hybrid mobility in unknown environments, showing that hybrid mobility uses up to five times less energy than flying only1.1Video at https://youtu.be/nlGfYehTLpg.

Aggressive maneuvering amongst obstacles could enable advanced capabilities for quadrotors in applications such as search and rescue, surveillance, inspection, and situations where rapid flight is required in cluttered environments. Previous works have treated quadrotors as differentially flat systems, and this property has been exploited widely to design simple algorithms that generate dynamically feasible trajectories and to enable hierarchical control. The differentially flat property allows the full state of the quadrotor to be extracted from the reduced dimensional space of x, y, z, yaw and their derivatives. This differential flatness transformation has a number of singularities, however, as well as stability issues when controlling near these singularities. Many methods have been described in the literature to address these; however, they all have limitations when exploring the full flight envelope of a quadrotor, including roll or pitch angles past 90°, and during inverted flight. In this paper, we review these existing methods and then introduce our method, which combines multiple methods to provide a highly-robust differential flatness transformation that addresses most of these issues. Our approach is demonstrated enabling highly-aggressive quadrotor flight in both simulations and real-world experiments.

Asymptotically-optimal motion planners such as RRT* have been shown to incrementally approximate the shortest path between start and goal states. Once an initial solution is found, their performance can be dramatically improved by restricting subsequent samples to regions of the state space that can potentially improve the current solution. When the motion-planning problem lies in a Euclidean space, this region Xinf, called the informed set, can be sampled directly. However, when planning with differential constraints in non-Euclidean state spaces, no analytic solutions exists to sampling Xinfdirectly. State-of-the-art approaches to sampling Xinfin such domains such as Hierarchical Rejection Sampling (HRS) may still be slow in high -dimensional state space. This may cause the planning algorithm to spend most of its time trying to produces samples in Xinfrather than explore it. In this paper, we suggest an alternative approach to produce samples in the informed set Xinffor a wide range of settings. Our main insight is to recast this problem as one of sampling uniformly within the sub-level-set of an implicit non-convex function. This recasting enables us to apply Monte Carlo sampling methods, used very effectively in the Machine Learning and Optimization communities, to solve our problem. We show for a wide range of scenarios that using our sampler can accelerate the convergence rate to high-quality solutions in high-dimensional problems.

Robots capable of switching between snake-like and bipedal motion have advantages of greater manoeuvrability. This paper introduces ReBiS (Reconfigurable Bipedal Snake) robot, a novel modular design mechanism which can quickly transform between various configurations without rearrangement of modules. This paper documents the design as well as the gaits implemented on ReBiS. Possible gaits are divided into three categories; snake gaits, transforming gaits and walking gaits. An example gait, belonging each of the three categories, is implemented and presented here. Experimental verification demonstrated that the reconfiguration of this robot is swift and without reshuffling of modules.